Road infrastructure indicators for trajectory prediction

Conference paper (2018)

Authors

F. Gaisser Intelligent Vehicles - Mechanical, Maritime and Materials Engineering
Pieter P. Jonker Robot Engineering Systems
Research Group
Intelligent Vehicles (Mechanical, Maritime and Materials Engineering) (TU Delft)
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Published Date

2018

Language

English

Reuse Rights

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Faculty

Mechanical, Maritime and Materials Engineering

Department

Cognitive Robotics

Research Group

Intelligent Vehicles

Abstract

Safe and comfortable path planning in a dynamic urban environment is essential to an autonomous vehicle. This requires the future trajectories of all other road users in the environment of the vehicle. These trajectories are predicted through modeling the motion and behaviour of these road users. In this work we state that for efficient trajectory prediction only motion indicators are not sufficient. Therefore, we propose using a curvilinear coordinate system with curvature as road infrastructure indicators to improve motion modeling and trajectory prediction. With experiments, we show that the curvilinear coordinate system with curvature sufficiently incorporates the road structure. Furthermore, we show that a sequence-tosequence RNN model is suitable to incorporate road curvature indicators directly into the modeling and prediction.

Files

08500678_1.pdf
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